Modulation Error Research Articles

The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with diffrent latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more effiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coeffient of effiency (E) were calculated to check the adequacy of the model. On the basis of coeffient of effiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/day

A Modified Expectation Maximization Approach for Process Data Rectification

Process measurements are contaminated by random and/or gross measuring errors, which degenerates performances of data-based strategies for enhancing process performances, such as online optimization and advanced control. Many approaches have been proposed to reduce the influence of measuring errors, among which expectation maximization (EM) is a novel and parameter-free one proposed recently. In this study, we studied the EM approach in detail and argued that the original EM approach is not feasible to rectify measurements contaminated by persistent biases, which is a pitfall of the original EM approach. So, we propose a modified EM approach here to circumvent this pitfall by fixing the standard deviation of random error mode. The modified EM approach was evaluated by several benchmark cases of process data rectification from literatures. The results show advantages of the proposed approach to the original EM in solving efficiency and performance of data rectification.

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors.

Survey on throughput enhancement techniques for real-time wireless link deployment

Over the recent decades, the emergence of advanced wireless technologies has attracted many researchers and industries to explore in the field of next generation wireless links towards capacity optimisation with minimal energy requirements. This gave beginning to a new dimension of research in the field of 'Green yet high capacity links' which can connect our physical world to the digital reality with a support from environment friendly networks. The phenomenal growth in wireless applications and technology development also demands a step up in link capacity. This paper focuses on reviewing recent concepts and methodologies to improve link capacity for the next generation wireless systems. The main aim of this paper is to provide a conceptual understanding about the impact of modulation techniques and error control codes towards capacity improvement.

An error detection method for real-time EPID-based treatment delivery quality assurance.

To quantify the error detection power of a new treatment delivery error detection method. The method validates monitor unit (MU) resolved beam apertures using real-time EPID images. The on-board EPID imager was used to measure cine-EPID (~10Hz) images for 27 beams from 15 VMAT/SBRT clinical treatment plans and five nonclinical plans. For each frame acquisition, planned apertures were interpolated from the treatment plan multileaf collimator (MLC) positions expected during the frame acquisition interval. Inaccurate deliveries were identified by monitoring in-aperture missed fluence and out-of-aperture excess fluence beyond a specified buffer. Delivery errors were simulated by perturbing the planned MLC positions before comparison with nonperturbed measured apertures. Systematic 1-5mm MLC leaf shifts were used to train a logistic regression model to determine the error detection threshold. Model accuracy was monitored using tenfold cross-validation. The model's error detection ability was tested with other error modes: plan control point (CP) weight perturbations, collimator rotations, random MLC leaf position errors, EPID imager shift, and stuck MLC leaf. The error detection accuracy was evaluated using the Matthews correlation coefficient (MCC) and the false positive rate (FPR). Per-beam error thresholds of >1, >5, and >10% errant frames were tested to label per-beam errors. The model also was tested for its ability to distinguish five cases with highly similar plans and compared with gamma analysis. Delivery errors were detected by monitoring intended per-frame images with a 2mm MLC buffer. Frame-by-frame aperture errors were identified with an optimal threshold of 0.3% of the expected aperture area. The per-frame FPR was 0.02%. The MCC was 1.00 (perfect classification) for detection based on 1% of frames for random CP weight shift, 3mm random MLC shifts, 90° and 180° collimator rotations, and an MLC leaf stuck after 10% of the beam delivery. The MCC for 2°, 4°, and 8° collimator rotation were 0.53, 0.76, and 0.96, respectively, for the 1% of beam delivery threshold. The 3mm EPID shift had poor detection, with a minimum MCC of 0.14. The highly similar plans were reliably detected by the aperture check but were not detectable with gamma analysis. The high error detection sensitivity and low FPR makes the aperture check error detection method well suited to pretreatment and during-treatment beam delivery quality assurance (QA). The aperture check detects subtle beam delivery errors, including those resulting from MLC leaf positioning deviations, CP MU shifts, and stuck MLC leaves. Furthermore, the method can distinguish between highly similar treatment plans. Since the aperture check method monitors for the aperture shapes over a given MU interval, it is also sensitive to errors in MU per CP, without requiring dosimetric calibration of the EPID. The aperture check is one part of a Swiss cheese error detection scheme, which provides redundant error testing of multiple error modes, including nonaperture related errors. The rapid error detection, at 1% of a beam's delivery, make the aperture check a potential candidate for QA of on-line adaptive radiotherapy, or other situations in which pretreatment delivery QA is impractical.

Placental Changes in Anemia, Gestational Diabetes and Pregnancy Induced Hypertension

Background: To assess the changes occurring in the placenta due to anemia, pregnancy induced hypertension (PIH), Gestational Diabetes Mellitus (GDM) and whether these changes were reversible with treatment when they were diagnosed in early stages. Subjects and Methods: The study is conducted in the Department of Obstetrics and Gynecology, DVVPF’s Medical College, Ahmednagar on 200 anemic patients, 180 PIH patients and 650 GDM patients. Placentae were collected, stored in a 10% formalin solution. Morphological changes in respect to shape, weight, diameter, thickness, number of cotyledons and site of insertion of the cord are observed and documented. Results: In anemic patients who has undergone treatment, there were a smaller number of irregular (37/100) and round (63/100) round placentae were seen, whereas in the untreated anemic group, round (09/100) round and irregular (91/100) irregular placentae were seen. In PIH group with treatment, round (57/90) placentae and irregular (33/90) placentae were seen, whereas, in the untreated group, 13/100 round/oval placentae were observed and 77/100 irregular placentae were seen. Conclusion: Morphology of placenta is on error mode in untreated anemia, PIH and GDM. Diagnosis in the first trimester and on consequent treatment till the delivery is resulting in decreasing of irregular morphology of placenta and wellbeing of newborns.

QAlign: aligning nanopore reads accurately using current-level modeling.

Efficient and accurate alignment of DNA/RNA sequence reads to each other or to a reference genome/transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this article, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome/transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner. We show that QAlign is able to improve alignment rates from around 80% up to 90% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2, 2.5 and 10.8% in three real datasets for read-to-read alignment. Read-to-transcriptome alignment rates are improved from 51.6% to 75.4% and 82.6% to 90% in two real datasets. https://github.com/joshidhaivat/QAlign.git. Supplementary data are available at Bioinformatics online.

Loss-tolerant quantum key distribution with mixed signal states

The security of measurement device-independent quantum key distribution (MDI QKD) relies on a thorough characterization of one's optical source output, especially any noise in the state preparation process. Here, we provide an extension of the loss-tolerant protocol [Phys. Rev. A 90, 052314 (2014)], a leading proof technique for analyzing the security of QKD, to MDI QKD protocols that employ mixed signal states. We first reframe the core of the proof technique, noting its generalization to treat $d$-dimensional signal encodings. Concentrating on the qubit signal state case, we find that the mixed states can be interpreted as providing Alice and Bob with a virtual shield system they can employ to reduce Eve's knowledge of the secret key. We then introduce a simple semidefinite programming method for optimizing the virtual operations they can perform on the shield system to yield a higher key rate, along with an example calculation of fundamentally achievable key rates in the case of random polarization modulation error.

On the behavior of ocean analysis and forecast error covariance in the presence of baroclinic instability

The properties of the expected analysis and forecast error covariance matrices are explored using a novel method based on the tangent linearization and adjoint of a 4-dimensional variational (4D-Var) data assimilation system. The method is applied to the mesoscale circulation that develops in the presence of a baroclinically unstable mid-latitude ocean temperature front using a series of paternal twin experiments that employ both strong and weak constraint 4D-Var. Adopting the traditional view of Empirical Orthogonal Functions (EOFs) of a covariance matrix as the semi-major axes of a multi-dimensional hyper-ellipsoid, variations in the volume of the analysis and forecast error hyper-ellipsoids are explored which provides information about the flow of probability through state–space. The complementary variations in the expected total variance of the covariance matrix are also investigated. Two different kinds of behavior are identified that are associated with either the demise or growth of baroclinic instabilities. In both cases, the volume of the hyper-ellipsoid decreases during the 4D-Var analysis cycle. During the subsequent forecasts, the volume of the forecast error hyper-ellipsoid initially continues to collapse under both scenarios. During this time, the hyper-ellipsoid becomes increasingly elongated along some of the semi-major axes as forecast errors grow in preferential directions. Growth in these directions is controlled by the most unstable error modes, and projection of forecast error on to the precursors of these modes has been shown previously to be characterized by upscale energy transfer and non-normal processes. For the case of the growing wave, the forecast error hyper-ellipsoid continues to collapse through to the end of the forecast period. However, for the decaying wave, the hyper-ellipsoid may undergo expansion at longer forecast lead times.

Detecting and tracking drift in quantum information processors

If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

Turbulence mitigation in a 28 GHz radio‐over‐free‐space optics link using an integrated Mach–Zehnder interferometer and a diversity combining receiver

The authors propose an integrated Mach–Zehnder interferometer and diversity combining receiver to mitigate the atmospheric turbulence-induced fading in a millimetre-wave (mmW) radio-over-free-space optics (RoFSO) link. They use a carrier frequency of 28 GHz as recommended for the fifth-generation wireless access networks and consider two optical mmW signal generation schemes, namely double-sideband (DSB) and single-sideband (SSB). In direct detection (DD)-based RoFSO, the link performance is limited by atmospheric turbulence. They show that the proposed Rx can overcome this detrimental effect, which is verified by investigation of a 10 Gb/s 16-quadrature amplitude modulation orthogonal frequency-division-multiplexing signal at 28 GHz over a 1 km free-space optics link under weak and strong turbulence regimes. For the DSB scenario, the proposed Rx offers improved error vector magnitudes of about 0.8 and 5.7%, and modulation error ratios of 1.3 and 4.9 dB under weak and strong turbulence regimes, respectively, compared with the DD receiver (DD Rx). For the SSB scenario under weak turbulence, the proposed Rx achieves a 4 dB improvement in the receiver sensitivity and four orders of magnitude enhancement in the bit error rate over the DD Rx. The proposed Rx can be integrated on a single chip for further cost reduction.

Time‐domain resonant characteristics between the disturbances and the RS422 communication signals in Tesla pulse driver, and analysis on the caused RS422 communication interference

The communication circuit network is widely employed for signal transmission in high-voltage pulse driver system, for commanding, controlling, measurement & communication. The electromagnetic compatibility of the communication circuit network mainly decides the stability and reliability of the high-voltage pulse driver system. Aiming at the electromagnetic interference (EMI) problems of the RS422 communication circuit in the Tesla pulse driver, the generated disturbance sources and characteristics from the Tesla pulse driver itself are analysed in five divided work stages, and two typical simplified EMI coupling modes for the communication circuit are also presented in this Letter. In the RS422 communication cable circuits between the subsystems of the pulse driver, the time-domain resonant characteristics of the communication signal and the conducting disturbances are analysed. The error and failure modes of the RS422 communication data caused by the full time-domain conducting interference are also revealed. Evaluation and calculation method for the time-domain resonant probability and the interference probability of the RS422 communication signals is also put forward. The communication interference probability is also calculated under different repetition work modes of the Tesla pulse driver. In view of the time-domain resonance phenomenon, some effective ways for time-domain resonance elimination and interference compression are put forward.

Adaptive shared control strategy for lane changing assistance system via multi-mode switching

To address the lateral control issue between the automatic controller and the driver for lane changing assistance system, we present a novel multi-mode adaptive shared control strategy in optimal control framework. Different from traditional approaches that achieve shared control through adjusting control authority allocation, we aim at designing a human-friendly adaptive shared control scheme which can provide the driver with assistance in accordance with driving demands via multi-mode switching. Three control modes are proposed: 1. minimum-lateral error mode; 2. minimum-lateral acceleration mode; 3. minimum-interference mode. Meanwhile, we develop a hybrid model based on hidden markov model (HMM) and support vector machine (SVM) to recognize the optimum control mode. Mathematically, the shared control scheme is described as an optimal control problem, which is then addressed by using the improved Radau pseudospectral method (RPM). Numerical analysis and the driver-in-the-loop (DIL) tests show that the proposed shared control strategy has the advantage of ensuring driving safety while reducing driver’s workload.

High-Resolution ISAR Imaging with Modified Joint Range Spatial-Variant Autofocus and Azimuth Scaling.

Well-focused and accurately scaled high-resolution inverse synthetic aperture radar (ISAR) images provide a sound basis for feature extraction and target recognition. This paper proposes a novel high-resolution ISAR imaging algorithm, namely modified joint range spatial-variant autofocus and azimuth scaling algorithm (MJAAS). After motion compensation, the shift of the equivalent rotational center (ERC) of the target destroys the linear relationship between the azimuth chirp rates (ACR) of echo signals and the range coordinates of scattering points, thereby leading to the failure of azimuth scaling. Accordingly, a new joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model is established, serving as the basis of MJAAS. By recourse to the Davidon-Fletcher-Powell (DFP) algorithm, MJAAS can jointly estimate the ERCP and ERV by solving a minimum entropy optimization problem, so as to simultaneously achieve accurate azimuth scaling and range spatial-variant autofocus, which further improves the image focusing performance. MJAAS is not restricted by the modes of motion errors (coherent or non-coherent) and the motion compensation methods, so it can be widely applied to real data with the advantages of strong practicality and high accuracy. Extensive experimental results based on both simulated and real data are provided to corroborate the effectiveness of the proposed algorithm.

Study of Mesh Stiffness of Spur Gear Tooth by Considering Pitting Defect under Dynamic Load Conditions

Gear drives are one of the most essential mechanism of power transmission systems in numerous industrial applications such as automobiles, aerospace and wind turbines, etc. As the speed of gear transmission increases, the study of the dynamic behavior of the gears are more important in the gear design. Gear mesh stiffness plays an eminent role in gear dynamics and it varies in the existence of gear fault such as pitting, spalling and crack. The dynamic performance of the gears are affected by module, contact ratio, pressure angle and transmission error, etc. In order to understand the dynamic properties of spur gear system, it is necessary to calculate the mesh stiffness of the gear tooth pair effectively. In this paper, a comparative study has been carried out using FEM between healthy gear and fault gear by considering pitting defect. Gear mesh stiffness of healthy and fault gear has been compared under dynamic load distribution in gear tooth contacts.

Modulation and RS-CC rate specifications in WiMAX IEEE 802.16 Standard with MATLAB Simulink model

The intensive use of broadband system leading to focusing of telecommunication development industries, which are characterized by high quality features. Forthisissue, high transmission abilities in a new technologies systems have been designed. the WiMAX concept were proposed where a wireless transmission infrastructure that allows a fast deployment as well as low maintenance costs. Based on the IEEE 802.16-2004 standard, in this paper an end-to-end baseband model of the physical layer of a wireless metropolitan area network (WMAN) have been implemented using MATALB Simulink, according to the IEEE® 802.16-2004 standard. Supporting all of the mandatory coding and modulation options. It also illustrates Space-Time Block Coding (STBC), an optional transmit diversity scheme specified for use on the downlink. it illustrates the use of digital pre-distortion, a technique for extending the linear range of a nonlinear amplifier. Also modulation sets have been changed and tested in the model to reduce running time and power consumption. Finally, measurements of Error vector magnitude EVM and modulation error ratio MER were performed.

Actuator and sensor placement for closed-loop control of convective instabilities

This work deals with the characterization of the closed-loop control performance aiming at the delay of transition. We focus on convective wavepackets, typical of the initial stages of transition to turbulence, starting with the linearized Kuramoto–Sivashinsky equation as a model problem representative of the transitional 2D boundary layer; its simplified structure and reduced order provide a manageable framework for the study of fundamental concepts involving the control of linear wavepackets. The characterization is then extended to the 2D Blasius boundary layer. The objective of this study is to explore how the sensor–actuator placement affects the optimal control problem, formulated using linear quadratic Gaussian (LQG) regulators. This is carried out by evaluating errors of the optimal estimator at positions where control gains are significant, through a proposed metric, labelled as $$\gamma $$ . Results show, in quantitative manner, why some choices of sensor–actuator placement are more effective than others for flow control: good (respectively, bad) closed-loop performance is obtained when estimation errors are low (respectively, high) in the regions with significant gains in the full-state-feedback problem. Unsatisfactory performance is further understood as dominant estimation error modes that overlap spatially with control gains, which shows directions for improvement of a given set-up by moving sensors or actuators. The proposed metric and analysis explain most trends in closed-loop performance as a function of sensor and actuator position, obtained for the model problem and for the 2D Blasius boundary layer. The spatial characterization of the $$\gamma $$ -metric provides thus a valuable and intuitive tool for the problem of sensor–actuator placement, targeting here transition delay but possibly extending to other amplifier-type flows.

Nonlinear Phase Estimation and Compensation for FMCW Ladar Based on Synchrosqueezing Wavelet Transform

Frequency modulation continuous wave (FMCW) laser radar (Ladar) is a new radar system suitable for long-range detection and high-resolution imaging. However, the transmitted signal inevitably suffers from nonlinear frequency modulation errors which reduce the quality of Ladar imaging. We propose a nonlinear phase estimation and compensation method based on synchrosqueezing wavelet transform (SST). We first use SST to synchrosqueeze the dechirp signal containing a reference range in time-frequency domain and obtain the time-frequency information of the reference dechirp signal. As SST could aggregate the time-frequency distribution of noise, the proposed method is effective even in a noisy environment. A nonlinearity model in time-frequency domain is then built to estimate the nonlinear phase of the transmitted signals by using the time-frequency information of the reference dechirp signal. For the case of long-range detection, we adopt a residual video phase filtering to convert the nonlinear phase of the dechirp signal to range-independent phase errors. Finally, the nonlinearity of the dechirp signal is compensated using the estimated nonlinear phase of the transmitted signals. The experimental and real data tests show that the proposed method effectively improves the resolution of long-range Ladar imaging by compensating for the nonlinearity of the dechirp signal. Its advantage is the effectiveness for noisy and multicomponent FMCW Ladar signals.

Facilitating Inversion of the Error Covariance Models for the Wide-Swath Altimeters

Wide-swath satellite altimeter observations are contaminated by errors caused by the uncertainties in the geometry and orientation of the on-board interferometer. These errors are strongly correlated across the track, while also having similar error structures in the along-track direction. We describe a method for modifying the geometric component of the error covariance matrix which facilitates accuracy in the removal of the respective error modes from the signal and improves computational efficiency of the data assimilation schemes involving wide-swath altimeter observations. The method has been tested using the Surface Water and Ocean Topography simulator. We show substantial computer cost savings in the pseudo-inversion of the respective error covariance matrix. This efficiency improvement comes with a few per cent error in the approximation of the original covariance model simulating uncertainties in the geometry and orientation of the on-board interferometer.

Human reliability assessment for complex physical operations in harsh operating conditions

This paper presents a methodology for quantifying the effect of harsh environmental conditions on the reliability of human actions in performing complex physical operations. A review of current human reliability techniques confirms that there is a lack of methodology for quantifying human errors while conducting complex physical operations in extreme environments. The developed methodology is based on a hierarchical Bayesian network accounting for causal dependencies among environmental factors, human error modes, and scenario-based activities. Also, a new model is developed with three reference points (awareness of the situation, access to a system, and action) that derives human error modes (HEMs) from physiological failure mechanisms and helps an analyst identify the root causes of human errors. The proposed methodology is applied to estimate the likelihood of human error in two different scenarios in harsh operating conditions in floating offshore structures. The two scenarios are a set of different human activities in a workplace under defined operational and environmental conditions. The proposed methodology helps enhance the safety of human performance while considering effective physical factors. It will also help to reform current regulations for working in harsh environments.